在复杂环境下设计鲁棒的关键点贴片，提升机器人视觉感知稳定性。
Design and Identification of Keypoint Patches in Unstructured Environments
- 设计四种简洁关键点贴片，适应尺度、旋转和相机投影变化。
- 定制Superpoint网络，在模糊与阴影下仍能稳定检测关键点。
- 适用于需要高可靠视觉感知的自主机器人系统开发。
可靠的目標感知對於自主機器人的穩定運作至關重要。一種常見方法是圖像中的關鍵點識別，因其可直接將原始影像映射為二維坐標，有利於與定位、路徑規劃等算法整合。本研究針對雜亂環境中關鍵點貼片的設計與檢測進行深入分析，探討模糊與陰影等因素對檢測的干擾。提出四種簡潔但差異化的設計方案，僅使用少量像素即可適應多樣的尺度、旋轉及相機投影變化。同時，針對不同類型的圖像退化，對Superpoint網絡進行定制優化，以確保檢測魯棒性。通過真實世界視頻測試驗證了所提方法的有效性，展現其在基於視覺的自主系統中的應用潛力。
原文摘要 · Abstract (English)
Reliable perception of targets is crucial for the stable operation of autonomous robots. A widely preferred method is keypoint identification in an image, as it allows direct mapping from raw images to 2D coordinates, facilitating integration with other algorithms like localization and path planning. In this study, we closely examine the design and identification of keypoint patches in cluttered environments, where factors such as blur and shadows can hinder detection. We propose four simple yet distinct designs that consider various scale, rotation and camera projection using a limited number of pixels. Additionally, we customize the Superpoint network to ensure robust detection under various types of image degradation. The effectiveness of our approach is demonstrated through real-world video tests, highlighting potential for vision-based autonomous systems.
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